Question-answering services have become increasingly popular and widely-used in a large variety of fields to facilitate access to the information world. However, providing satisfactory question-answering services to users is becoming more challenging. Conventional approaches for handling a question-answering task may include, for example, collecting predefined question-answer pairs to provide question-answering services, and searching for an answer and ranking the answer to match the question.
However, in many cases, such conventional approaches cannot offer an answer to the question input by a user that meets user expectations. For example, there is a limit with respect to predefining a large quantity of question-answer pairs, and a limit with respect to preparing comprehensive question-answer pairs to meet the needs of users with different types of questions. Also, a conventional question-answering service may require a user to “effectively” ask a question, i.e., if the question more accurately expresses the user's intention, the more likely the user gets an appropriate answer to the question. However, in many cases, the user may not be familiar with the field and/or terms related to the question. In such cases, the user cannot may not be able to submit an “effective” question and, as such, the probability of obtaining an appropriate or satisfactory answer may decrease.